






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_						:						List[Any]						      =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A_						:						Dict						      =					[]
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
   )
   rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
   rename_keys.append(
       (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
   )
   rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
   rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
   )
   rename_keys.append(
       (
           F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
           F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
       )
   )
   rename_keys.append(
       (
           F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
           F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
       )
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
   )

   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
   )
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
   )
   rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
   rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
   rename_keys.append(
       (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[Any]:
  UpperCamelCase_:   str        =							state_dict.pop(UpperCAmelCase__    )
  UpperCamelCase_:   Union[str, Any]        =							val

def     snake_case   (UpperCAmelCase__    )			->							int:
  UpperCamelCase_:   Optional[Any]        =							OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      UpperCamelCase_:   int        =							key.replace('backbone.0.body'      ,       'backbone.conv_encoder.model'    )
      UpperCamelCase_:   str        =							value
    else:
      UpperCamelCase_:   Optional[int]        =							value

  return new_state_dict

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=False    )			->							Union[str, Any]:
  UpperCamelCase_:   List[Any]        =							''
  if is_panoptic:
    UpperCamelCase_:   Union[str, Any]        =							'conditional_detr.'

  # first: transformer encoder
  for i in range(6    ):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    UpperCamelCase_:   Tuple        =							state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''    )
    UpperCamelCase_:   Optional[int]        =							state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''    )
    # next, add query, keys and values (in that order) to the state dict
    UpperCamelCase_:   str        =							in_proj_weight[:2_5_6, :]
    UpperCamelCase_:   int        =							in_proj_bias[:2_5_6]
    UpperCamelCase_:   Optional[Any]        =							in_proj_weight[2_5_6:5_1_2, :]
    UpperCamelCase_:   List[str]        =							in_proj_bias[2_5_6:5_1_2]
    UpperCamelCase_:   Dict        =							in_proj_weight[-2_5_6:, :]
    UpperCamelCase_:   List[str]        =							in_proj_bias[-2_5_6:]

def     snake_case   ()			->							int:
  UpperCamelCase_:   Optional[int]        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCamelCase_:   List[Any]        =							Image.open(requests.get(UpperCAmelCase__      ,       stream=UpperCAmelCase__    ).raw    )

  return im

@torch.no_grad()
def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[str]:
  UpperCamelCase_:   List[Any]        =							ConditionalDetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    UpperCamelCase_:   Dict        =							'resnet101'
  if "dc5" in model_name:
    UpperCamelCase_:   Union[str, Any]        =							True
  UpperCamelCase_:   Optional[int]        =							'panoptic' in model_name
  if is_panoptic:
    UpperCamelCase_:   Optional[Any]        =							2_5_0
  else:
    UpperCamelCase_:   Optional[Any]        =							9_1
    UpperCamelCase_:   Any        =							'huggingface/label-files'
    UpperCamelCase_:   List[str]        =							'coco-detection-id2label.json'
    UpperCamelCase_:   Any        =							json.load(open(hf_hub_download(UpperCAmelCase__      ,       UpperCAmelCase__      ,       repo_type='dataset'    )      ,       'r'    )    )
    UpperCamelCase_:   str        =							{int(UpperCAmelCase__    ): v for k, v in idalabel.items()}
    UpperCamelCase_:   List[str]        =							idalabel
    UpperCamelCase_:   List[Any]        =							{v: k for k, v in idalabel.items()}

  # load image processor
  UpperCamelCase_:   List[Any]        =							'coco_panoptic' if is_panoptic else 'coco_detection'
  UpperCamelCase_:   List[Any]        =							ConditionalDetrImageProcessor(format=UpperCAmelCase__    )

  # prepare image
  UpperCamelCase_:   Optional[int]        =							prepare_img()
  UpperCamelCase_:   Union[str, Any]        =							image_processor(images=UpperCAmelCase__      ,       return_tensors='pt'    )
  UpperCamelCase_:   Optional[int]        =							encoding['pixel_values']

  logger.info(F'''Converting model {model_name}...'''    )

  # load original model from torch hub
  UpperCamelCase_:   Tuple        =							torch.hub.load('DeppMeng/ConditionalDETR'      ,       UpperCAmelCase__      ,       pretrained=UpperCAmelCase__    ).eval()
  UpperCamelCase_:   Union[str, Any]        =							conditional_detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      UpperCamelCase_:   List[str]        =							'conditional_detr.' + src
    rename_key(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
  UpperCamelCase_:   Union[str, Any]        =							rename_backbone_keys(UpperCAmelCase__    )
  # query, key and value matrices need special treatment
  read_in_q_k_v(UpperCAmelCase__      ,       is_panoptic=UpperCAmelCase__    )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  UpperCamelCase_:   Optional[Any]        =							'conditional_detr.model.' if is_panoptic else 'model.'
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith('conditional_detr'    )
          and not key.startswith('class_labels_classifier'    )
          and not key.startswith('bbox_predictor'    )
      ):
        UpperCamelCase_:   int        =							state_dict.pop(UpperCAmelCase__    )
        UpperCamelCase_:   Union[str, Any]        =							val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        UpperCamelCase_:   int        =							state_dict.pop(UpperCAmelCase__    )
        UpperCamelCase_:   Dict        =							val
      elif key.startswith('bbox_attention'    ) or key.startswith('mask_head'    ):
        continue
      else:
        UpperCamelCase_:   List[str]        =							state_dict.pop(UpperCAmelCase__    )
        UpperCamelCase_:   Union[str, Any]        =							val
    else:
      if not key.startswith('class_labels_classifier'    ) and not key.startswith('bbox_predictor'    ):
        UpperCamelCase_:   Optional[int]        =							state_dict.pop(UpperCAmelCase__    )
        UpperCamelCase_:   List[str]        =							val
    # finally, create HuggingFace model and load state dict
  UpperCamelCase_:   List[Any]        =							ConditionalDetrForSegmentation(UpperCAmelCase__    ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCAmelCase__    )
  model.load_state_dict(UpperCAmelCase__    )
  model.eval()
  model.push_to_hub(repo_id=UpperCAmelCase__      ,       organization='DepuMeng'      ,       commit_message='Add model'    )
  # verify our conversion
  UpperCamelCase_:   Union[str, Any]        =							conditional_detr(UpperCAmelCase__    )
  UpperCamelCase_:   Optional[int]        =							model(UpperCAmelCase__    )
  assert torch.allclose(outputs.logits      ,       original_outputs['pred_logits']      ,       atol=1E-4    )
  assert torch.allclose(outputs.pred_boxes      ,       original_outputs['pred_boxes']      ,       atol=1E-4    )
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks      ,       original_outputs['pred_masks']      ,       atol=1E-4    )

  # Save model and image processor
  logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''    )
  Path(UpperCAmelCase__    ).mkdir(exist_ok=UpperCAmelCase__    )
  model.save_pretrained(UpperCAmelCase__    )
  image_processor.save_pretrained(UpperCAmelCase__    )


if __name__ == "__main__":
   A_						:						Dict						      =					argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       default='conditional_detr_resnet50',
       type=str,
       help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   A_						:						Dict						      =					parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
